ESAPD | | | 95.11 1 | 95.65 1 | 94.48 1 | 97.96 3 | 98.62 1 | 96.45 1 | 92.82 2 | 96.24 3 | 90.25 5 | 96.16 2 | 93.09 1 | 93.32 3 | 93.93 13 | 92.02 19 | 96.07 19 | 99.50 3 |
|
HSP-MVS | | | 94.69 2 | 95.39 2 | 93.88 4 | 96.78 14 | 98.11 5 | 94.75 7 | 90.91 9 | 96.89 2 | 89.12 10 | 96.98 1 | 89.47 9 | 94.76 1 | 95.24 2 | 93.29 10 | 96.98 7 | 97.73 30 |
|
CNVR-MVS | | | 94.53 3 | 94.85 4 | 94.15 3 | 98.03 2 | 98.59 2 | 95.56 3 | 92.91 1 | 94.86 8 | 88.46 11 | 91.32 17 | 90.83 5 | 94.03 2 | 95.20 3 | 94.16 4 | 95.89 24 | 99.01 12 |
|
APDe-MVS | | | 94.31 4 | 94.30 7 | 94.33 2 | 97.57 7 | 98.06 7 | 95.79 2 | 91.98 5 | 95.50 6 | 92.19 1 | 95.25 3 | 87.97 14 | 92.93 4 | 93.01 20 | 91.02 35 | 95.52 28 | 99.29 5 |
|
MCST-MVS | | | 94.10 5 | 94.77 5 | 93.31 6 | 98.31 1 | 98.34 3 | 95.43 4 | 92.54 3 | 94.41 13 | 83.05 27 | 91.38 15 | 90.97 4 | 92.24 9 | 95.05 5 | 94.02 5 | 98.31 1 | 99.20 7 |
|
HPM-MVS++ | | | 94.04 6 | 94.96 3 | 92.96 8 | 97.93 4 | 97.71 13 | 94.65 9 | 91.01 8 | 95.91 4 | 87.43 13 | 93.52 8 | 92.63 2 | 92.29 8 | 94.22 12 | 92.34 16 | 94.47 47 | 98.37 22 |
|
NCCC | | | 93.59 7 | 94.00 9 | 93.10 7 | 97.90 5 | 97.93 9 | 95.40 5 | 92.39 4 | 94.47 12 | 84.94 18 | 91.21 18 | 89.32 10 | 92.53 6 | 93.90 14 | 92.98 12 | 95.44 30 | 98.22 24 |
|
APD-MVS | | | 93.47 8 | 93.44 13 | 93.50 5 | 97.06 10 | 97.09 22 | 95.27 6 | 91.47 6 | 95.71 5 | 89.57 7 | 93.66 6 | 86.28 19 | 92.81 5 | 92.06 27 | 90.70 37 | 94.83 43 | 98.60 18 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
SD-MVS | | | 93.36 9 | 94.33 6 | 92.22 10 | 94.68 38 | 97.89 11 | 94.56 10 | 90.89 10 | 94.80 9 | 90.04 6 | 93.53 7 | 90.14 7 | 89.78 17 | 92.74 22 | 92.17 17 | 93.35 97 | 99.07 10 |
|
SMA-MVS | | | 93.14 10 | 93.96 10 | 92.17 11 | 97.64 6 | 97.82 12 | 94.28 14 | 90.32 11 | 94.72 10 | 85.70 17 | 87.64 25 | 90.68 6 | 91.15 13 | 94.28 11 | 93.86 7 | 93.97 56 | 98.72 16 |
|
TSAR-MVS + MP. | | | 93.07 11 | 93.53 12 | 92.53 9 | 94.23 41 | 97.54 17 | 94.75 7 | 89.87 13 | 95.26 7 | 89.20 9 | 93.16 9 | 88.19 13 | 92.15 10 | 91.79 31 | 89.65 48 | 94.99 39 | 99.16 8 |
|
SteuartSystems-ACMMP | | | 92.31 12 | 93.31 14 | 91.15 18 | 96.88 12 | 97.36 18 | 93.95 16 | 89.44 15 | 92.62 22 | 83.20 24 | 94.34 5 | 85.55 21 | 88.95 24 | 93.07 19 | 91.90 23 | 94.51 45 | 98.30 23 |
Skip Steuart: Steuart Systems R&D Blog. |
ACMMP_Plus | | | 92.16 13 | 92.91 17 | 91.28 17 | 96.95 11 | 97.36 18 | 93.66 17 | 89.23 17 | 93.33 17 | 83.71 22 | 90.53 19 | 86.84 16 | 90.39 14 | 93.30 18 | 91.56 28 | 93.74 65 | 97.43 37 |
|
HFP-MVS | | | 92.02 14 | 92.13 19 | 91.89 15 | 97.16 9 | 96.46 35 | 93.57 18 | 87.60 21 | 93.79 15 | 88.17 12 | 93.15 10 | 83.94 32 | 91.19 12 | 90.81 40 | 89.83 43 | 93.66 70 | 96.94 54 |
|
train_agg | | | 91.99 15 | 93.71 11 | 89.98 23 | 96.42 23 | 97.03 24 | 94.31 13 | 89.05 18 | 93.33 17 | 77.75 41 | 95.06 4 | 88.27 12 | 88.38 30 | 92.02 28 | 91.41 30 | 94.00 54 | 98.84 15 |
|
DeepC-MVS_fast | | 86.59 2 | 91.69 16 | 91.39 22 | 92.05 14 | 97.43 8 | 96.92 27 | 94.05 15 | 90.23 12 | 93.31 20 | 83.19 25 | 77.91 39 | 84.23 28 | 92.42 7 | 94.62 8 | 94.83 2 | 95.00 38 | 97.88 27 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
zzz-MVS | | | 91.59 17 | 91.12 23 | 92.13 12 | 96.76 15 | 96.68 30 | 93.39 19 | 88.00 20 | 93.63 16 | 90.76 4 | 83.97 32 | 85.33 23 | 89.89 16 | 91.60 33 | 89.65 48 | 94.00 54 | 96.97 52 |
|
TSAR-MVS + GP. | | | 91.29 18 | 93.11 16 | 89.18 29 | 87.81 85 | 96.21 41 | 92.51 28 | 83.83 40 | 94.24 14 | 83.77 21 | 91.87 14 | 89.62 8 | 90.07 15 | 90.40 43 | 90.31 39 | 97.09 6 | 99.10 9 |
|
ACMMPR | | | 91.15 19 | 91.44 21 | 90.81 19 | 96.61 17 | 96.25 39 | 93.09 20 | 87.08 23 | 93.32 19 | 84.78 19 | 92.08 13 | 82.10 38 | 89.71 18 | 90.24 44 | 89.82 44 | 93.61 75 | 96.30 68 |
|
DeepPCF-MVS | | 86.71 1 | 91.00 20 | 94.05 8 | 87.43 40 | 95.58 32 | 98.17 4 | 86.22 67 | 88.59 19 | 97.01 1 | 76.77 46 | 85.11 30 | 88.90 11 | 87.29 35 | 95.02 6 | 94.69 3 | 90.15 186 | 99.48 4 |
|
TSAR-MVS + ACMM | | | 90.98 21 | 93.18 15 | 88.42 34 | 95.69 30 | 96.73 29 | 94.52 11 | 86.97 26 | 92.99 21 | 76.32 47 | 92.31 12 | 86.64 17 | 84.40 57 | 92.97 21 | 92.02 19 | 92.62 138 | 98.59 19 |
|
MP-MVS | | | 90.81 22 | 91.45 20 | 90.06 22 | 96.59 18 | 96.33 38 | 92.46 29 | 87.19 22 | 90.27 34 | 82.54 31 | 91.38 15 | 84.88 25 | 88.27 31 | 90.58 42 | 89.30 53 | 93.30 99 | 97.44 35 |
|
CP-MVS | | | 90.57 23 | 90.68 25 | 90.44 20 | 96.13 25 | 95.90 46 | 92.77 25 | 86.86 27 | 92.12 25 | 84.19 20 | 89.18 23 | 82.37 36 | 89.43 22 | 89.65 52 | 88.43 57 | 93.27 101 | 97.13 46 |
|
MSLP-MVS++ | | | 90.33 24 | 88.82 34 | 92.10 13 | 96.52 21 | 95.93 42 | 94.35 12 | 86.26 28 | 88.37 47 | 89.24 8 | 75.94 44 | 82.60 35 | 89.71 18 | 89.45 54 | 92.17 17 | 96.51 14 | 97.24 42 |
|
CANet | | | 89.98 25 | 90.42 29 | 89.47 28 | 94.13 42 | 98.05 8 | 91.76 34 | 83.27 43 | 90.87 31 | 81.90 34 | 72.32 51 | 84.82 26 | 88.42 28 | 94.52 9 | 93.78 8 | 97.34 4 | 98.58 20 |
|
PGM-MVS | | | 89.97 26 | 90.64 27 | 89.18 29 | 96.53 20 | 95.90 46 | 93.06 21 | 82.48 51 | 90.04 36 | 80.37 36 | 92.75 11 | 80.96 43 | 88.93 25 | 89.88 49 | 89.08 54 | 93.69 69 | 95.86 74 |
|
PHI-MVS | | | 89.88 27 | 92.75 18 | 86.52 50 | 94.97 35 | 97.57 16 | 89.99 45 | 84.56 36 | 92.52 23 | 69.72 75 | 90.35 20 | 87.11 15 | 84.89 50 | 91.82 30 | 92.37 15 | 95.02 37 | 97.51 33 |
|
CSCG | | | 89.81 28 | 89.69 30 | 89.96 24 | 96.55 19 | 97.90 10 | 92.89 23 | 87.06 24 | 88.74 45 | 86.17 14 | 78.24 38 | 86.53 18 | 84.75 53 | 87.82 76 | 90.59 38 | 92.32 144 | 98.01 26 |
|
X-MVS | | | 89.73 29 | 90.65 26 | 88.66 32 | 96.44 22 | 95.93 42 | 92.26 31 | 86.98 25 | 90.73 32 | 76.32 47 | 89.56 22 | 82.05 39 | 86.51 41 | 89.98 47 | 89.60 50 | 93.43 92 | 96.72 62 |
|
EPNet | | | 89.30 30 | 90.89 24 | 87.44 39 | 95.67 31 | 96.81 28 | 91.13 37 | 83.12 45 | 91.14 28 | 76.31 51 | 87.60 26 | 80.40 46 | 84.45 55 | 92.13 26 | 91.12 34 | 93.96 58 | 97.01 50 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
DeepC-MVS | | 84.14 3 | 88.80 31 | 88.03 38 | 89.71 26 | 94.83 36 | 96.56 31 | 92.57 27 | 89.38 16 | 89.25 42 | 79.59 38 | 70.02 60 | 77.05 56 | 88.24 32 | 92.44 24 | 92.79 13 | 93.65 73 | 98.10 25 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CDPH-MVS | | | 88.76 32 | 90.43 28 | 86.81 46 | 96.04 27 | 96.53 34 | 92.95 22 | 85.95 30 | 90.36 33 | 67.93 80 | 85.80 29 | 80.69 44 | 83.82 58 | 90.81 40 | 91.85 26 | 94.18 50 | 96.99 51 |
|
3Dnovator+ | | 81.14 5 | 88.59 33 | 87.49 40 | 89.88 25 | 95.83 29 | 96.45 37 | 91.94 33 | 82.41 52 | 87.09 51 | 85.94 16 | 62.80 88 | 85.37 22 | 89.46 20 | 91.51 34 | 91.89 25 | 93.72 67 | 97.30 40 |
|
ACMMP | | | 88.48 34 | 88.71 35 | 88.22 36 | 94.61 39 | 95.53 50 | 90.64 41 | 85.60 32 | 90.97 29 | 78.62 40 | 89.88 21 | 74.20 67 | 86.29 42 | 88.16 74 | 86.37 77 | 93.57 77 | 95.86 74 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
AdaColmap | | | 88.46 35 | 85.75 53 | 91.62 16 | 96.25 24 | 95.35 53 | 90.71 39 | 91.08 7 | 90.22 35 | 86.17 14 | 74.33 48 | 73.67 70 | 92.00 11 | 86.31 94 | 85.82 85 | 93.52 80 | 94.53 91 |
|
MVS_0304 | | | 88.43 36 | 89.46 31 | 87.21 41 | 91.85 54 | 97.60 14 | 92.62 26 | 81.10 58 | 87.16 50 | 73.80 56 | 72.19 53 | 83.36 34 | 87.03 36 | 94.64 7 | 93.67 9 | 96.88 8 | 97.64 32 |
|
3Dnovator | | 80.58 8 | 88.20 37 | 86.53 46 | 90.15 21 | 96.86 13 | 96.46 35 | 91.97 32 | 83.06 46 | 85.16 57 | 83.66 23 | 62.28 91 | 82.15 37 | 88.98 23 | 90.99 38 | 92.65 14 | 96.38 18 | 96.03 72 |
|
CPTT-MVS | | | 88.17 38 | 87.84 39 | 88.55 33 | 93.33 44 | 93.75 64 | 92.33 30 | 84.75 35 | 89.87 38 | 81.72 35 | 83.93 33 | 81.12 42 | 88.45 27 | 85.42 104 | 84.07 104 | 90.72 178 | 96.72 62 |
|
MVS_111021_HR | | | 87.82 39 | 88.84 33 | 86.62 48 | 94.42 40 | 97.36 18 | 88.21 54 | 83.26 44 | 83.42 60 | 72.52 64 | 82.63 34 | 76.93 57 | 84.95 49 | 91.93 29 | 91.15 33 | 96.39 17 | 98.49 21 |
|
DELS-MVS | | | 87.75 40 | 86.92 44 | 88.71 31 | 94.69 37 | 97.34 21 | 92.78 24 | 84.50 37 | 77.87 77 | 81.94 33 | 67.17 65 | 75.49 62 | 82.84 63 | 95.38 1 | 95.93 1 | 95.55 27 | 99.27 6 |
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023 |
MVSTER | | | 87.68 41 | 89.12 32 | 86.01 52 | 88.11 82 | 90.05 106 | 89.28 48 | 77.05 80 | 91.37 26 | 79.97 37 | 76.70 42 | 85.25 24 | 84.89 50 | 93.53 15 | 91.41 30 | 96.73 10 | 95.55 79 |
|
MVS_111021_LR | | | 87.58 42 | 88.67 36 | 86.31 51 | 92.58 48 | 95.89 48 | 86.20 68 | 82.49 50 | 89.08 44 | 77.47 43 | 86.20 28 | 74.22 66 | 85.49 46 | 90.03 46 | 88.52 55 | 93.66 70 | 96.74 61 |
|
QAPM | | | 87.06 43 | 86.46 47 | 87.75 37 | 96.63 16 | 97.09 22 | 91.71 35 | 82.62 49 | 80.58 69 | 71.28 69 | 66.04 70 | 84.24 27 | 87.01 37 | 89.93 48 | 89.91 42 | 97.26 5 | 97.44 35 |
|
PVSNet_BlendedMVS | | | 86.98 44 | 87.05 42 | 86.90 43 | 93.03 45 | 96.98 25 | 86.57 63 | 81.82 54 | 89.78 39 | 82.78 29 | 71.54 54 | 66.07 94 | 80.73 78 | 93.46 16 | 91.97 21 | 96.45 15 | 99.53 1 |
|
PVSNet_Blended | | | 86.98 44 | 87.05 42 | 86.90 43 | 93.03 45 | 96.98 25 | 86.57 63 | 81.82 54 | 89.78 39 | 82.78 29 | 71.54 54 | 66.07 94 | 80.73 78 | 93.46 16 | 91.97 21 | 96.45 15 | 99.53 1 |
|
OMC-MVS | | | 86.38 46 | 86.21 50 | 86.57 49 | 92.30 50 | 94.35 59 | 87.60 57 | 83.51 42 | 92.32 24 | 77.37 44 | 72.27 52 | 77.83 51 | 86.59 40 | 87.62 79 | 85.95 82 | 92.08 148 | 93.11 120 |
|
HQP-MVS | | | 86.17 47 | 87.35 41 | 84.80 57 | 91.41 57 | 92.37 85 | 91.05 38 | 84.35 39 | 88.52 46 | 64.21 84 | 87.05 27 | 68.91 87 | 84.80 52 | 89.12 57 | 88.16 61 | 92.96 122 | 97.31 39 |
|
canonicalmvs | | | 85.93 48 | 86.26 49 | 85.54 53 | 88.94 68 | 95.44 51 | 89.56 46 | 76.01 87 | 87.83 48 | 77.70 42 | 76.43 43 | 68.66 89 | 87.80 34 | 87.02 83 | 91.51 29 | 93.25 105 | 96.95 53 |
|
MAR-MVS | | | 85.65 49 | 86.30 48 | 84.88 56 | 95.51 34 | 95.89 48 | 86.50 65 | 76.71 81 | 89.23 43 | 68.59 77 | 70.93 58 | 74.49 64 | 88.55 26 | 89.40 55 | 90.30 40 | 93.42 93 | 93.88 110 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
PCF-MVS | | 82.38 4 | 85.52 50 | 84.41 58 | 86.81 46 | 91.51 56 | 96.23 40 | 90.27 42 | 89.81 14 | 77.87 77 | 70.67 70 | 69.20 62 | 77.86 50 | 85.55 45 | 85.92 99 | 86.38 76 | 93.03 119 | 97.43 37 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
CLD-MVS | | | 85.43 51 | 84.24 59 | 86.83 45 | 87.69 88 | 93.16 72 | 90.01 44 | 82.72 48 | 87.17 49 | 79.28 39 | 71.43 57 | 65.81 96 | 86.02 43 | 87.33 81 | 86.96 70 | 95.25 34 | 97.83 29 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
OpenMVS | | 77.91 11 | 85.09 52 | 83.42 62 | 87.03 42 | 96.12 26 | 96.55 33 | 89.36 47 | 81.59 56 | 79.19 72 | 75.20 53 | 55.84 119 | 79.04 49 | 84.45 55 | 88.47 66 | 89.35 52 | 95.48 29 | 95.48 80 |
|
TSAR-MVS + COLMAP | | | 84.93 53 | 85.79 52 | 83.92 60 | 90.90 59 | 93.57 67 | 89.25 49 | 82.00 53 | 91.29 27 | 61.66 89 | 88.25 24 | 59.46 115 | 86.71 39 | 89.79 50 | 87.09 67 | 93.01 120 | 91.09 136 |
|
TAPA-MVS | | 80.99 7 | 84.83 54 | 84.42 57 | 85.31 54 | 91.89 53 | 93.73 65 | 88.53 53 | 82.80 47 | 89.99 37 | 69.78 74 | 71.53 56 | 75.03 63 | 85.47 47 | 86.26 95 | 84.54 99 | 93.39 95 | 89.90 143 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PLC | | 81.02 6 | 84.81 55 | 81.81 76 | 88.31 35 | 93.77 43 | 90.35 101 | 88.80 51 | 84.47 38 | 86.76 52 | 82.17 32 | 66.56 67 | 71.01 80 | 88.41 29 | 85.48 102 | 84.28 102 | 92.26 146 | 88.21 168 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
CNLPA | | | 84.72 56 | 82.14 72 | 87.73 38 | 92.85 47 | 93.83 63 | 84.70 85 | 85.07 33 | 90.90 30 | 83.16 26 | 56.28 115 | 71.53 76 | 88.14 33 | 84.19 112 | 84.00 107 | 92.48 141 | 94.26 96 |
|
MVS_Test | | | 84.60 57 | 85.13 55 | 83.99 59 | 88.17 80 | 95.27 54 | 88.21 54 | 73.15 107 | 84.31 59 | 70.55 72 | 68.67 63 | 68.78 88 | 86.99 38 | 91.71 32 | 91.90 23 | 96.84 9 | 95.27 84 |
|
diffmvs | | | 83.81 58 | 84.78 56 | 82.69 64 | 86.06 109 | 94.03 60 | 86.46 66 | 72.43 114 | 85.71 55 | 75.29 52 | 65.48 75 | 79.49 48 | 81.39 68 | 85.55 101 | 86.98 68 | 94.48 46 | 96.20 70 |
|
CANet_DTU | | | 83.33 59 | 86.59 45 | 79.53 87 | 88.88 69 | 94.87 57 | 86.63 62 | 68.85 144 | 85.45 56 | 50.54 155 | 77.86 40 | 69.94 84 | 85.62 44 | 92.63 23 | 90.88 36 | 96.63 11 | 94.46 92 |
|
DI_MVS_plusplus_trai | | | 83.32 60 | 82.53 70 | 84.25 58 | 86.26 105 | 93.66 66 | 90.23 43 | 77.16 79 | 77.05 84 | 74.06 55 | 53.74 128 | 74.33 65 | 83.61 60 | 91.40 36 | 89.82 44 | 94.17 51 | 97.73 30 |
|
DWT-MVSNet_training | | | 82.66 61 | 83.34 65 | 81.87 68 | 88.71 70 | 92.63 77 | 82.07 100 | 72.21 116 | 86.37 53 | 72.64 59 | 64.51 79 | 71.44 78 | 80.35 81 | 84.43 110 | 87.73 63 | 95.27 31 | 96.25 69 |
|
PVSNet_Blended_VisFu | | | 82.55 62 | 83.70 61 | 81.21 74 | 89.66 63 | 95.15 56 | 82.41 98 | 77.36 77 | 72.53 104 | 73.64 57 | 61.15 96 | 77.19 55 | 70.35 148 | 91.31 37 | 89.72 47 | 93.84 61 | 98.85 14 |
|
PMMVS | | | 82.26 63 | 85.48 54 | 78.51 96 | 85.92 110 | 91.92 89 | 78.30 143 | 70.77 130 | 86.30 54 | 61.11 94 | 82.46 35 | 70.88 81 | 84.70 54 | 88.05 75 | 84.78 97 | 90.24 185 | 93.98 102 |
|
ACMP | | 79.58 9 | 82.23 64 | 81.82 75 | 82.71 63 | 88.15 81 | 90.95 98 | 85.23 77 | 78.52 63 | 81.70 66 | 72.52 64 | 78.41 37 | 60.63 110 | 80.48 80 | 82.88 122 | 83.44 112 | 91.37 166 | 94.70 88 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
CHOSEN 280x420 | | | 82.15 65 | 85.87 51 | 77.80 99 | 86.54 99 | 93.42 69 | 81.74 101 | 59.96 198 | 78.99 74 | 63.99 85 | 74.50 47 | 83.95 31 | 80.99 74 | 89.53 53 | 85.01 90 | 93.56 79 | 95.71 78 |
|
LGP-MVS_train | | | 82.12 66 | 82.57 69 | 81.59 69 | 89.26 67 | 90.23 103 | 88.76 52 | 78.05 67 | 81.26 67 | 61.64 90 | 79.52 36 | 62.11 105 | 79.59 85 | 85.20 105 | 84.68 98 | 92.27 145 | 95.02 86 |
|
FMVSNet3 | | | 81.93 67 | 81.98 73 | 81.88 67 | 79.49 140 | 87.02 129 | 88.15 56 | 72.57 110 | 83.02 62 | 72.63 61 | 56.55 111 | 73.48 71 | 82.34 66 | 91.49 35 | 91.20 32 | 96.07 19 | 91.13 135 |
|
OPM-MVS | | | 81.34 68 | 78.18 96 | 85.02 55 | 91.27 58 | 91.78 91 | 90.66 40 | 83.62 41 | 62.39 141 | 65.91 81 | 63.35 85 | 64.33 101 | 85.03 48 | 87.77 77 | 85.88 84 | 93.66 70 | 91.75 133 |
|
IS_MVSNet | | | 80.92 69 | 84.14 60 | 77.16 104 | 87.43 89 | 93.90 62 | 80.44 106 | 74.64 99 | 75.05 90 | 61.10 95 | 65.59 72 | 76.89 58 | 67.39 159 | 90.88 39 | 90.05 41 | 91.95 152 | 96.62 65 |
|
ACMM | | 78.09 10 | 80.91 70 | 78.39 94 | 83.86 61 | 89.61 66 | 87.71 123 | 85.16 78 | 80.67 59 | 79.04 73 | 74.18 54 | 63.82 83 | 60.84 109 | 82.59 64 | 84.33 111 | 83.59 110 | 90.96 173 | 89.39 151 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
EPP-MVSNet | | | 80.82 71 | 82.79 67 | 78.52 94 | 86.31 104 | 92.37 85 | 79.83 114 | 74.51 100 | 73.79 99 | 64.46 83 | 67.01 66 | 80.63 45 | 74.33 109 | 85.63 100 | 84.35 101 | 91.68 158 | 95.79 77 |
|
conf0.002 | | | 80.80 72 | 80.30 82 | 81.38 72 | 88.59 71 | 93.19 71 | 85.12 79 | 78.10 65 | 70.15 110 | 61.55 91 | 63.30 86 | 62.66 104 | 81.11 69 | 88.74 63 | 86.94 71 | 93.79 63 | 97.15 44 |
|
CostFormer | | | 80.72 73 | 81.81 76 | 79.44 89 | 86.50 100 | 91.65 93 | 84.31 87 | 59.84 199 | 80.86 68 | 72.69 58 | 62.46 90 | 73.74 68 | 79.93 83 | 82.58 125 | 84.50 100 | 93.37 96 | 96.90 57 |
|
GBi-Net | | | 80.72 73 | 80.49 80 | 81.00 79 | 78.18 144 | 86.19 147 | 86.73 59 | 72.57 110 | 83.02 62 | 72.63 61 | 56.55 111 | 73.48 71 | 80.99 74 | 86.57 88 | 86.83 72 | 94.89 40 | 90.77 137 |
|
test1 | | | 80.72 73 | 80.49 80 | 81.00 79 | 78.18 144 | 86.19 147 | 86.73 59 | 72.57 110 | 83.02 62 | 72.63 61 | 56.55 111 | 73.48 71 | 80.99 74 | 86.57 88 | 86.83 72 | 94.89 40 | 90.77 137 |
|
UGNet | | | 80.71 76 | 83.09 66 | 77.93 98 | 87.02 93 | 92.71 75 | 80.28 110 | 76.53 83 | 73.83 98 | 71.35 68 | 70.07 59 | 73.71 69 | 58.93 186 | 87.39 80 | 86.97 69 | 93.48 89 | 96.94 54 |
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022 |
tfpn111 | | | 80.42 77 | 79.77 87 | 81.18 75 | 88.42 74 | 92.55 81 | 85.12 79 | 77.94 69 | 70.15 110 | 61.00 98 | 74.56 45 | 51.22 136 | 81.11 69 | 88.23 68 | 84.80 93 | 93.50 85 | 96.90 57 |
|
CHOSEN 1792x2688 | | | 80.23 78 | 79.16 88 | 81.48 70 | 91.97 51 | 96.56 31 | 86.18 69 | 75.40 95 | 76.17 87 | 61.32 93 | 37.43 206 | 61.08 108 | 76.52 99 | 92.35 25 | 91.64 27 | 97.46 3 | 98.86 13 |
|
conf0.01 | | | 80.10 79 | 79.04 90 | 81.34 73 | 88.56 72 | 93.09 73 | 85.12 79 | 78.08 66 | 70.15 110 | 61.43 92 | 60.90 97 | 58.54 118 | 81.11 69 | 88.66 64 | 84.80 93 | 93.74 65 | 97.14 45 |
|
thres100view900 | | | 79.83 80 | 77.79 100 | 82.21 65 | 88.42 74 | 93.54 68 | 87.07 58 | 81.11 57 | 70.15 110 | 61.01 96 | 56.65 108 | 51.22 136 | 81.78 67 | 89.77 51 | 85.95 82 | 93.84 61 | 97.26 41 |
|
Effi-MVS+ | | | 79.80 81 | 80.04 83 | 79.52 88 | 85.53 111 | 93.31 70 | 85.28 75 | 70.68 132 | 74.15 94 | 58.79 108 | 62.03 93 | 60.51 111 | 83.37 61 | 88.41 67 | 86.09 81 | 93.49 88 | 95.80 76 |
|
FC-MVSNet-train | | | 79.54 82 | 78.20 95 | 81.09 78 | 86.55 98 | 88.63 119 | 79.96 112 | 78.53 62 | 70.90 108 | 68.24 78 | 65.87 71 | 56.45 127 | 80.29 82 | 86.20 97 | 84.08 103 | 92.97 121 | 95.31 83 |
|
test-LLR | | | 79.52 83 | 83.42 62 | 74.97 114 | 81.79 125 | 91.26 94 | 76.17 167 | 70.57 133 | 77.71 79 | 52.14 133 | 66.26 68 | 77.47 53 | 73.10 115 | 87.02 83 | 87.16 65 | 96.05 22 | 97.02 48 |
|
FMVSNet2 | | | 79.24 84 | 78.14 97 | 80.53 83 | 78.18 144 | 86.19 147 | 86.73 59 | 71.91 120 | 72.97 101 | 70.48 73 | 50.63 138 | 66.56 93 | 80.99 74 | 90.10 45 | 89.77 46 | 94.89 40 | 90.77 137 |
|
TESTMET0.1,1 | | | 79.15 85 | 83.42 62 | 74.18 124 | 79.81 138 | 91.26 94 | 76.17 167 | 67.83 155 | 77.71 79 | 52.14 133 | 66.26 68 | 77.47 53 | 73.10 115 | 87.02 83 | 87.16 65 | 96.05 22 | 97.02 48 |
|
tfpn200view9 | | | 79.05 86 | 77.21 102 | 81.18 75 | 88.42 74 | 92.55 81 | 85.12 79 | 77.94 69 | 70.15 110 | 61.01 96 | 56.65 108 | 51.22 136 | 81.11 69 | 88.23 68 | 84.80 93 | 93.50 85 | 96.90 57 |
|
conf200view11 | | | 79.04 87 | 77.21 102 | 81.18 75 | 88.42 74 | 92.55 81 | 85.12 79 | 77.94 69 | 70.15 110 | 61.00 98 | 56.65 108 | 51.22 136 | 81.11 69 | 88.23 68 | 84.80 93 | 93.50 85 | 96.90 57 |
|
thresconf0.02 | | | 78.87 88 | 80.50 79 | 76.96 105 | 87.88 84 | 91.71 92 | 82.90 97 | 78.51 64 | 67.91 119 | 50.85 148 | 74.56 45 | 69.93 85 | 67.32 160 | 86.86 86 | 85.65 86 | 94.32 49 | 86.89 176 |
|
PatchMatch-RL | | | 78.75 89 | 76.47 110 | 81.41 71 | 88.53 73 | 91.10 96 | 78.09 147 | 77.51 76 | 77.33 81 | 71.98 66 | 64.38 81 | 48.10 152 | 82.55 65 | 84.06 113 | 82.35 126 | 89.78 188 | 87.97 170 |
|
LS3D | | | 78.72 90 | 75.79 116 | 82.15 66 | 91.91 52 | 89.39 115 | 83.66 90 | 85.88 31 | 76.81 85 | 59.22 107 | 57.67 105 | 58.53 119 | 83.72 59 | 82.07 130 | 81.63 139 | 88.50 197 | 84.39 184 |
|
thres200 | | | 78.69 91 | 76.71 106 | 80.99 81 | 88.35 78 | 92.56 79 | 86.03 70 | 77.94 69 | 66.27 122 | 60.66 100 | 56.08 116 | 51.11 140 | 79.45 86 | 88.23 68 | 85.54 88 | 93.52 80 | 97.20 43 |
|
tpmp4_e23 | | | 78.57 92 | 78.48 93 | 78.68 92 | 85.38 113 | 89.14 117 | 84.69 86 | 60.32 197 | 78.81 75 | 70.65 71 | 57.89 103 | 65.54 97 | 79.63 84 | 80.09 149 | 83.24 115 | 91.41 165 | 94.63 90 |
|
IB-MVS | | 74.10 12 | 78.52 93 | 78.51 92 | 78.52 94 | 90.15 61 | 95.39 52 | 71.95 186 | 77.53 75 | 74.95 91 | 77.25 45 | 58.93 101 | 55.92 128 | 58.37 188 | 79.01 169 | 87.89 62 | 95.88 25 | 97.47 34 |
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021 |
EPNet_dtu | | | 78.49 94 | 81.96 74 | 74.45 121 | 92.57 49 | 88.74 118 | 82.98 92 | 78.83 61 | 83.28 61 | 44.64 193 | 77.40 41 | 67.73 90 | 53.98 198 | 85.44 103 | 84.91 91 | 93.71 68 | 86.22 178 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
thres400 | | | 78.39 95 | 76.39 111 | 80.73 82 | 88.02 83 | 92.94 74 | 84.77 84 | 78.88 60 | 65.20 130 | 59.70 104 | 55.20 121 | 50.85 141 | 79.45 86 | 88.81 60 | 84.81 92 | 93.57 77 | 96.91 56 |
|
UA-Net | | | 78.30 96 | 80.92 78 | 75.25 113 | 87.42 90 | 92.48 84 | 79.54 126 | 75.49 94 | 60.47 147 | 60.52 101 | 68.44 64 | 84.08 30 | 57.54 189 | 88.54 65 | 88.45 56 | 90.96 173 | 83.97 189 |
|
Vis-MVSNet (Re-imp) | | | 78.28 97 | 82.68 68 | 73.16 144 | 86.64 96 | 92.68 76 | 78.07 148 | 74.48 101 | 74.05 95 | 53.47 124 | 64.22 82 | 76.52 59 | 54.28 194 | 88.96 59 | 88.29 59 | 92.03 150 | 94.00 101 |
|
tfpn_ndepth | | | 78.22 98 | 78.84 91 | 77.49 101 | 88.32 79 | 90.95 98 | 80.79 105 | 76.31 85 | 74.24 93 | 59.50 106 | 69.52 61 | 60.02 114 | 67.11 161 | 85.06 106 | 82.95 121 | 92.94 127 | 89.18 156 |
|
MSDG | | | 78.11 99 | 73.17 134 | 83.86 61 | 91.78 55 | 86.83 134 | 85.25 76 | 86.02 29 | 72.84 102 | 69.69 76 | 51.43 135 | 54.00 133 | 77.61 91 | 81.95 134 | 82.27 128 | 92.83 134 | 82.91 194 |
|
HyFIR lowres test | | | 78.08 100 | 76.81 104 | 79.56 86 | 90.77 60 | 94.64 58 | 82.97 93 | 69.85 137 | 69.81 116 | 59.53 105 | 33.52 211 | 64.66 98 | 78.97 88 | 88.77 62 | 88.38 58 | 95.27 31 | 97.86 28 |
|
test-mter | | | 77.90 101 | 82.44 71 | 72.60 149 | 78.52 142 | 90.24 102 | 73.85 179 | 65.31 173 | 76.37 86 | 51.29 137 | 65.58 73 | 75.94 61 | 71.36 128 | 85.98 98 | 86.26 78 | 95.26 33 | 96.71 64 |
|
view600 | | | 77.68 102 | 75.68 117 | 80.01 84 | 87.72 86 | 92.57 78 | 83.79 88 | 77.95 68 | 64.41 133 | 58.72 109 | 54.32 126 | 50.54 142 | 78.25 89 | 88.23 68 | 83.13 117 | 93.64 74 | 96.59 66 |
|
thres600view7 | | | 77.66 103 | 75.67 118 | 79.98 85 | 87.71 87 | 92.56 79 | 83.79 88 | 77.94 69 | 64.41 133 | 58.69 110 | 54.32 126 | 50.54 142 | 78.23 90 | 88.23 68 | 83.06 119 | 93.52 80 | 96.55 67 |
|
MS-PatchMatch | | | 77.47 104 | 76.48 109 | 78.63 93 | 89.89 62 | 90.42 100 | 85.42 74 | 69.53 139 | 70.79 109 | 60.43 102 | 50.05 140 | 70.62 83 | 70.66 141 | 86.71 87 | 82.54 123 | 95.86 26 | 84.23 185 |
|
tfpn | | | 77.45 105 | 76.23 113 | 78.87 91 | 87.15 92 | 91.90 90 | 82.17 99 | 76.59 82 | 62.98 139 | 56.93 113 | 53.08 132 | 57.31 124 | 76.41 101 | 87.26 82 | 85.20 89 | 93.95 59 | 95.89 73 |
|
Fast-Effi-MVS+ | | | 77.37 106 | 76.68 107 | 78.17 97 | 82.84 122 | 89.94 107 | 81.47 103 | 68.01 152 | 72.99 100 | 60.26 103 | 55.07 122 | 53.20 134 | 82.99 62 | 86.47 93 | 86.12 80 | 93.46 90 | 92.98 123 |
|
Vis-MVSNet | | | 77.24 107 | 79.99 86 | 74.02 128 | 84.62 116 | 93.92 61 | 80.33 109 | 72.55 113 | 62.58 140 | 55.25 118 | 64.45 80 | 69.49 86 | 57.00 190 | 88.78 61 | 88.21 60 | 94.36 48 | 92.54 125 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
view800 | | | 77.22 108 | 75.35 119 | 79.41 90 | 87.42 90 | 92.21 87 | 82.94 95 | 77.19 78 | 63.67 137 | 57.78 111 | 53.68 129 | 50.19 144 | 77.32 92 | 87.70 78 | 83.84 108 | 93.79 63 | 96.19 71 |
|
MDTV_nov1_ep13 | | | 77.20 109 | 80.04 83 | 73.90 130 | 82.22 123 | 90.14 104 | 79.25 132 | 61.52 191 | 78.63 76 | 56.98 112 | 65.52 74 | 72.80 74 | 73.05 117 | 80.93 142 | 83.20 116 | 90.36 182 | 89.05 158 |
|
EPMVS | | | 77.16 110 | 79.08 89 | 74.92 115 | 86.73 94 | 91.98 88 | 78.62 139 | 55.44 209 | 79.43 70 | 56.59 115 | 61.24 95 | 70.73 82 | 76.97 96 | 80.59 145 | 81.43 151 | 95.15 36 | 88.17 169 |
|
tpm cat1 | | | 76.93 111 | 76.19 114 | 77.79 100 | 85.08 115 | 88.58 120 | 82.96 94 | 59.33 200 | 75.72 89 | 72.64 59 | 51.25 136 | 64.41 100 | 75.74 104 | 77.90 178 | 80.10 174 | 90.97 172 | 95.35 81 |
|
PatchmatchNet | | | 76.85 112 | 80.03 85 | 73.15 145 | 84.08 118 | 91.04 97 | 77.76 152 | 55.85 208 | 79.43 70 | 52.74 129 | 62.08 92 | 76.02 60 | 74.56 107 | 79.92 150 | 81.41 152 | 93.92 60 | 90.29 142 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
IterMVS-LS | | | 76.80 113 | 76.33 112 | 77.35 103 | 84.07 119 | 84.11 168 | 81.54 102 | 68.52 146 | 66.17 123 | 61.74 88 | 57.84 104 | 64.31 102 | 74.88 105 | 83.48 120 | 86.21 79 | 93.34 98 | 92.16 128 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CDS-MVSNet | | | 76.57 114 | 76.78 105 | 76.32 108 | 80.94 132 | 89.75 111 | 82.94 95 | 72.64 109 | 59.01 158 | 62.95 87 | 58.60 102 | 62.67 103 | 66.91 163 | 86.26 95 | 87.20 64 | 91.57 160 | 93.97 103 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tpmrst | | | 76.27 115 | 77.65 101 | 74.66 117 | 86.13 107 | 89.53 114 | 79.31 131 | 54.91 210 | 77.19 83 | 56.27 116 | 55.87 118 | 64.58 99 | 77.25 93 | 80.85 143 | 80.21 171 | 94.07 52 | 95.32 82 |
|
dps | | | 75.76 116 | 75.02 122 | 76.63 107 | 84.51 117 | 88.12 121 | 77.51 154 | 58.33 202 | 75.91 88 | 71.98 66 | 57.37 106 | 57.85 121 | 76.81 98 | 77.89 179 | 78.40 183 | 90.63 179 | 89.63 146 |
|
tfpn1000 | | | 75.39 117 | 76.18 115 | 74.47 120 | 86.71 95 | 90.10 105 | 77.57 153 | 74.78 97 | 68.76 118 | 53.33 125 | 63.57 84 | 58.37 120 | 60.84 182 | 83.80 116 | 81.24 156 | 93.58 76 | 87.42 172 |
|
tfpnview11 | | | 74.85 118 | 75.06 121 | 74.61 118 | 86.58 97 | 89.54 113 | 79.98 111 | 75.81 89 | 64.95 132 | 47.47 172 | 64.85 76 | 54.72 129 | 63.86 170 | 84.54 109 | 82.20 130 | 93.97 56 | 84.64 181 |
|
CR-MVSNet | | | 74.84 119 | 77.91 98 | 71.26 171 | 81.77 127 | 85.52 156 | 78.32 141 | 54.14 212 | 74.05 95 | 51.09 141 | 50.00 141 | 71.38 79 | 70.77 138 | 86.48 91 | 84.03 105 | 91.46 164 | 93.92 106 |
|
Effi-MVS+-dtu | | | 74.57 120 | 74.60 126 | 74.53 119 | 81.38 129 | 86.74 136 | 80.39 108 | 67.70 156 | 67.36 121 | 53.06 126 | 59.86 99 | 57.50 122 | 75.84 103 | 80.19 147 | 78.62 181 | 88.79 196 | 91.95 132 |
|
tfpn_n400 | | | 74.36 121 | 74.39 128 | 74.32 122 | 86.37 102 | 89.86 108 | 79.71 116 | 75.69 91 | 60.00 149 | 47.47 172 | 64.85 76 | 54.72 129 | 63.70 173 | 83.80 116 | 83.35 113 | 92.96 122 | 84.16 186 |
|
tfpnconf | | | 74.36 121 | 74.39 128 | 74.32 122 | 86.37 102 | 89.86 108 | 79.71 116 | 75.69 91 | 60.00 149 | 47.47 172 | 64.85 76 | 54.72 129 | 63.70 173 | 83.80 116 | 83.35 113 | 92.96 122 | 84.16 186 |
|
RPSCF | | | 74.27 123 | 73.24 133 | 75.48 112 | 81.01 131 | 80.18 192 | 76.24 166 | 72.37 115 | 74.84 92 | 68.24 78 | 72.47 50 | 67.39 91 | 73.89 110 | 71.05 206 | 69.38 215 | 81.14 222 | 77.37 205 |
|
FMVSNet1 | | | 74.26 124 | 71.95 139 | 76.95 106 | 74.28 192 | 83.94 170 | 83.61 91 | 69.99 135 | 57.08 163 | 65.08 82 | 42.39 187 | 57.41 123 | 76.98 95 | 86.57 88 | 86.83 72 | 91.77 157 | 89.42 149 |
|
conf0.05thres1000 | | | 74.20 125 | 71.44 142 | 77.43 102 | 86.09 108 | 89.85 110 | 80.82 104 | 75.79 90 | 53.51 187 | 54.71 119 | 44.37 164 | 49.78 145 | 74.67 106 | 85.02 107 | 83.47 111 | 92.49 140 | 94.10 99 |
|
GA-MVS | | | 73.62 126 | 74.52 127 | 72.58 150 | 79.93 136 | 89.29 116 | 78.02 149 | 71.67 126 | 60.79 146 | 42.68 197 | 54.41 125 | 49.07 148 | 70.07 151 | 89.39 56 | 86.55 75 | 93.13 116 | 92.12 129 |
|
Fast-Effi-MVS+-dtu | | | 73.56 127 | 75.32 120 | 71.50 167 | 80.35 134 | 86.83 134 | 79.72 115 | 58.07 203 | 67.64 120 | 44.83 190 | 60.28 98 | 54.07 132 | 73.59 114 | 81.90 136 | 82.30 127 | 92.46 142 | 94.18 97 |
|
tpm | | | 73.50 128 | 74.85 123 | 71.93 161 | 83.19 121 | 86.84 133 | 78.61 140 | 55.91 207 | 65.64 125 | 48.90 164 | 56.30 114 | 61.09 107 | 72.31 119 | 79.10 168 | 80.61 170 | 92.68 136 | 94.35 95 |
|
RPMNet | | | 73.46 129 | 77.85 99 | 68.34 179 | 81.71 128 | 85.52 156 | 73.83 180 | 50.54 220 | 74.05 95 | 46.10 182 | 53.03 133 | 71.91 75 | 66.31 165 | 83.55 119 | 82.18 131 | 91.55 162 | 94.71 87 |
|
USDC | | | 73.43 130 | 72.31 137 | 74.73 116 | 80.86 133 | 86.21 145 | 80.42 107 | 71.83 122 | 71.69 106 | 46.94 176 | 59.60 100 | 42.58 189 | 76.47 100 | 82.66 124 | 81.22 158 | 91.88 154 | 82.24 199 |
|
pmmvs4 | | | 73.38 131 | 71.53 141 | 75.55 111 | 75.95 172 | 85.24 160 | 77.25 157 | 71.59 127 | 71.03 107 | 63.10 86 | 49.09 146 | 44.22 177 | 73.73 113 | 82.04 131 | 80.18 172 | 91.68 158 | 88.89 162 |
|
UniMVSNet_NR-MVSNet | | | 73.11 132 | 72.59 135 | 73.71 132 | 76.90 153 | 86.58 140 | 77.01 158 | 75.82 88 | 65.59 126 | 48.82 165 | 50.97 137 | 48.42 150 | 71.61 125 | 79.19 166 | 83.03 120 | 92.11 147 | 94.37 93 |
|
FMVSNet5 | | | 72.83 133 | 73.89 131 | 71.59 165 | 67.42 211 | 76.28 207 | 75.88 171 | 63.74 183 | 77.27 82 | 54.59 121 | 53.32 130 | 71.48 77 | 73.85 111 | 81.95 134 | 81.69 137 | 94.06 53 | 75.20 211 |
|
PatchT | | | 72.66 134 | 76.58 108 | 68.09 181 | 79.02 141 | 86.09 151 | 59.81 209 | 51.78 218 | 72.00 105 | 51.09 141 | 46.84 151 | 66.70 92 | 70.77 138 | 86.48 91 | 84.03 105 | 96.07 19 | 93.92 106 |
|
ACMH | | 71.22 14 | 72.65 135 | 70.13 150 | 75.59 110 | 86.19 106 | 86.14 150 | 75.76 172 | 77.63 74 | 54.79 180 | 46.16 181 | 53.28 131 | 47.28 154 | 77.24 94 | 78.91 171 | 81.18 160 | 90.57 180 | 89.33 152 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
IterMVS | | | 72.43 136 | 74.05 130 | 70.55 175 | 80.34 135 | 81.17 187 | 77.44 155 | 61.00 193 | 63.57 138 | 46.82 178 | 55.88 117 | 59.09 117 | 65.03 167 | 83.15 121 | 83.83 109 | 92.67 137 | 91.65 134 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
ACMH+ | | 72.14 13 | 72.38 137 | 69.34 161 | 75.93 109 | 85.21 114 | 84.89 163 | 76.96 161 | 76.04 86 | 59.76 151 | 51.63 136 | 50.37 139 | 48.69 149 | 76.90 97 | 76.06 187 | 78.69 179 | 88.85 195 | 86.90 175 |
|
DU-MVS | | | 72.19 138 | 71.35 143 | 73.17 143 | 75.95 172 | 86.02 152 | 77.01 158 | 74.42 102 | 65.39 128 | 48.82 165 | 49.10 144 | 42.81 187 | 71.61 125 | 78.67 172 | 83.10 118 | 91.22 168 | 94.37 93 |
|
UniMVSNet (Re) | | | 72.12 139 | 72.28 138 | 71.93 161 | 76.77 154 | 87.38 125 | 75.73 173 | 73.51 106 | 65.76 124 | 50.24 157 | 48.65 147 | 46.49 155 | 63.85 171 | 80.10 148 | 82.47 124 | 91.49 163 | 95.13 85 |
|
ADS-MVSNet | | | 72.11 140 | 73.72 132 | 70.24 176 | 81.24 130 | 86.59 139 | 74.75 176 | 50.56 219 | 72.58 103 | 49.17 162 | 55.40 120 | 61.46 106 | 73.80 112 | 76.01 188 | 78.14 184 | 91.93 153 | 85.86 179 |
|
gg-mvs-nofinetune | | | 72.10 141 | 74.79 124 | 68.97 178 | 83.31 120 | 95.22 55 | 85.66 73 | 48.77 222 | 35.68 222 | 22.17 231 | 30.49 215 | 77.73 52 | 76.37 102 | 94.30 10 | 93.03 11 | 97.55 2 | 97.05 47 |
|
TAMVS | | | 72.06 142 | 71.76 140 | 72.41 154 | 76.68 156 | 88.12 121 | 74.82 175 | 68.09 151 | 53.52 186 | 56.91 114 | 52.94 134 | 56.93 126 | 66.91 163 | 81.37 139 | 82.44 125 | 91.07 170 | 86.99 174 |
|
v6 | | | 72.04 143 | 70.26 146 | 74.11 125 | 76.46 160 | 87.06 126 | 79.60 118 | 71.75 123 | 59.48 153 | 52.69 130 | 44.61 157 | 45.79 160 | 71.01 136 | 79.57 156 | 81.45 149 | 93.16 111 | 93.85 113 |
|
v1neww | | | 72.02 144 | 70.23 148 | 74.10 126 | 76.45 161 | 87.06 126 | 79.59 121 | 71.75 123 | 59.35 154 | 52.60 131 | 44.59 159 | 45.74 161 | 71.06 133 | 79.57 156 | 81.46 147 | 93.16 111 | 93.84 114 |
|
v7new | | | 72.02 144 | 70.23 148 | 74.10 126 | 76.45 161 | 87.06 126 | 79.59 121 | 71.75 123 | 59.35 154 | 52.60 131 | 44.59 159 | 45.74 161 | 71.06 133 | 79.57 156 | 81.46 147 | 93.16 111 | 93.84 114 |
|
v2v482 | | | 71.73 146 | 69.80 153 | 73.99 129 | 75.88 179 | 86.66 138 | 79.58 124 | 71.90 121 | 57.58 162 | 50.41 156 | 45.35 154 | 43.24 185 | 73.05 117 | 79.69 151 | 82.18 131 | 93.08 118 | 93.87 111 |
|
test0.0.03 1 | | | 71.70 147 | 74.68 125 | 68.23 180 | 81.79 125 | 83.81 171 | 68.64 192 | 70.57 133 | 68.81 117 | 43.47 194 | 62.77 89 | 60.09 113 | 51.77 204 | 82.48 126 | 81.67 138 | 93.16 111 | 83.13 192 |
|
V42 | | | 71.58 148 | 70.11 151 | 73.30 141 | 75.66 185 | 86.68 137 | 79.17 134 | 69.92 136 | 59.29 157 | 52.80 128 | 44.36 165 | 45.66 163 | 68.83 153 | 79.48 162 | 81.49 146 | 93.44 91 | 93.82 116 |
|
v1 | | | 71.54 149 | 69.71 154 | 73.66 135 | 76.08 166 | 86.88 130 | 79.60 118 | 72.06 119 | 57.00 165 | 50.75 152 | 44.23 168 | 44.79 166 | 70.61 143 | 79.62 152 | 81.52 142 | 92.88 131 | 93.93 104 |
|
v1141 | | | 71.53 150 | 69.69 155 | 73.68 133 | 76.08 166 | 86.86 131 | 79.59 121 | 72.07 118 | 57.01 164 | 50.78 150 | 44.23 168 | 44.70 169 | 70.68 140 | 79.61 154 | 81.52 142 | 92.89 128 | 93.92 106 |
|
divwei89l23v2f112 | | | 71.53 150 | 69.69 155 | 73.68 133 | 76.09 165 | 86.86 131 | 79.60 118 | 72.08 117 | 56.96 166 | 50.78 150 | 44.24 167 | 44.70 169 | 70.65 142 | 79.62 152 | 81.53 140 | 92.89 128 | 93.93 104 |
|
v7 | | | 71.49 152 | 69.98 152 | 73.25 142 | 75.89 177 | 86.45 141 | 79.44 129 | 69.29 142 | 58.07 160 | 50.08 158 | 43.87 175 | 43.67 179 | 71.94 121 | 82.03 133 | 81.70 135 | 92.88 131 | 94.04 100 |
|
NR-MVSNet | | | 71.47 153 | 71.11 144 | 71.90 163 | 77.73 149 | 86.02 152 | 76.88 162 | 74.42 102 | 65.39 128 | 46.09 183 | 49.10 144 | 39.87 201 | 64.27 169 | 81.40 138 | 82.24 129 | 91.99 151 | 93.75 117 |
|
v8 | | | 71.42 154 | 69.69 155 | 73.43 139 | 76.45 161 | 85.12 162 | 79.53 127 | 67.47 159 | 59.34 156 | 52.90 127 | 44.60 158 | 45.82 159 | 71.05 135 | 79.56 159 | 81.45 149 | 93.17 109 | 91.96 131 |
|
v18 | | | 71.13 155 | 68.98 163 | 73.63 136 | 76.66 157 | 79.78 194 | 79.95 113 | 65.98 167 | 61.34 143 | 54.71 119 | 44.75 156 | 46.06 156 | 71.27 129 | 79.59 155 | 81.51 145 | 93.21 107 | 89.81 144 |
|
TranMVSNet+NR-MVSNet | | | 71.12 156 | 70.24 147 | 72.15 158 | 76.01 170 | 84.80 165 | 76.55 164 | 75.65 93 | 61.99 142 | 45.29 186 | 48.42 148 | 43.07 186 | 67.55 157 | 78.28 175 | 82.83 122 | 91.85 155 | 92.29 126 |
|
v10 | | | 70.97 157 | 69.44 158 | 72.75 146 | 75.90 176 | 84.58 167 | 79.43 130 | 66.45 164 | 58.07 160 | 49.93 159 | 43.87 175 | 43.68 178 | 71.91 122 | 82.04 131 | 81.70 135 | 92.89 128 | 92.11 130 |
|
v1144 | | | 70.93 158 | 69.42 160 | 72.70 147 | 75.48 186 | 86.26 143 | 79.22 133 | 69.39 141 | 55.61 177 | 48.05 170 | 43.47 181 | 42.55 190 | 71.51 127 | 82.11 129 | 81.74 134 | 92.56 139 | 94.17 98 |
|
v16 | | | 70.93 158 | 68.76 167 | 73.47 138 | 76.60 158 | 79.66 196 | 79.57 125 | 65.81 170 | 60.85 144 | 54.44 122 | 44.50 163 | 45.90 158 | 71.15 130 | 79.50 160 | 81.39 153 | 93.27 101 | 89.51 148 |
|
v17 | | | 70.82 160 | 68.69 168 | 73.31 140 | 76.53 159 | 79.67 195 | 79.45 128 | 65.80 171 | 60.32 148 | 53.75 123 | 44.51 162 | 45.92 157 | 71.09 132 | 79.49 161 | 81.38 154 | 93.26 104 | 89.54 147 |
|
Baseline_NR-MVSNet | | | 70.61 161 | 68.87 165 | 72.65 148 | 75.95 172 | 80.49 190 | 75.92 170 | 74.75 98 | 65.10 131 | 48.78 167 | 41.28 195 | 44.28 176 | 68.45 154 | 78.67 172 | 79.64 175 | 92.04 149 | 92.62 124 |
|
v148 | | | 70.34 162 | 68.46 170 | 72.54 152 | 76.04 169 | 86.38 142 | 74.83 174 | 72.73 108 | 55.88 176 | 55.26 117 | 43.32 184 | 43.49 180 | 64.52 168 | 76.93 185 | 80.11 173 | 91.85 155 | 93.11 120 |
|
v1192 | | | 70.32 163 | 68.77 166 | 72.12 160 | 74.76 188 | 85.62 155 | 78.73 137 | 68.53 145 | 55.08 179 | 46.34 180 | 42.39 187 | 40.67 197 | 71.90 123 | 82.27 127 | 81.53 140 | 92.43 143 | 93.86 112 |
|
v144192 | | | 70.10 164 | 68.55 169 | 71.90 163 | 74.55 189 | 85.67 154 | 77.81 150 | 68.22 150 | 54.65 181 | 46.91 177 | 42.76 185 | 41.27 196 | 70.95 137 | 80.48 146 | 81.11 165 | 92.96 122 | 93.90 109 |
|
pmmvs5 | | | 70.01 165 | 69.31 162 | 70.82 174 | 75.80 182 | 86.26 143 | 72.94 181 | 67.91 153 | 53.84 185 | 47.22 175 | 47.31 150 | 41.47 195 | 67.61 156 | 83.93 115 | 81.93 133 | 93.42 93 | 90.42 141 |
|
v15 | | | 70.00 166 | 67.82 175 | 72.55 151 | 76.06 168 | 79.37 198 | 79.10 135 | 65.30 174 | 56.89 167 | 51.18 139 | 43.96 174 | 44.76 167 | 70.52 145 | 79.40 163 | 81.22 158 | 93.13 116 | 89.14 157 |
|
V14 | | | 69.91 167 | 67.71 177 | 72.47 153 | 76.01 170 | 79.30 199 | 78.92 136 | 65.17 175 | 56.74 168 | 51.08 144 | 43.82 177 | 44.73 168 | 70.44 147 | 79.31 164 | 81.14 163 | 93.20 108 | 88.91 161 |
|
COLMAP_ROB | | 66.31 15 | 69.91 167 | 66.61 181 | 73.76 131 | 86.44 101 | 82.76 175 | 76.59 163 | 76.46 84 | 63.82 136 | 50.92 147 | 45.60 153 | 49.13 147 | 65.87 166 | 74.96 192 | 74.45 203 | 86.30 209 | 75.57 209 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
v1921920 | | | 69.85 169 | 68.38 171 | 71.58 166 | 74.35 190 | 85.39 158 | 77.78 151 | 67.88 154 | 54.64 182 | 45.39 185 | 42.11 190 | 39.97 200 | 71.10 131 | 81.68 137 | 81.17 162 | 92.96 122 | 93.69 119 |
|
v11 | | | 69.84 170 | 67.85 174 | 72.17 157 | 75.78 183 | 79.15 201 | 78.20 146 | 64.76 181 | 56.10 174 | 49.50 160 | 43.54 179 | 43.36 183 | 71.62 124 | 82.21 128 | 81.52 142 | 93.17 109 | 89.05 158 |
|
V9 | | | 69.79 171 | 67.57 178 | 72.38 155 | 75.95 172 | 79.21 200 | 78.72 138 | 65.06 176 | 56.51 170 | 51.06 145 | 43.66 178 | 44.70 169 | 70.28 149 | 79.22 165 | 81.06 166 | 93.24 106 | 88.67 165 |
|
v12 | | | 69.66 172 | 67.45 179 | 72.23 156 | 75.89 177 | 79.13 202 | 78.29 144 | 64.96 179 | 56.40 171 | 50.75 152 | 43.53 180 | 44.60 172 | 70.21 150 | 79.11 167 | 80.99 167 | 93.27 101 | 88.41 166 |
|
pm-mvs1 | | | 69.62 173 | 68.07 173 | 71.44 168 | 77.21 151 | 85.32 159 | 76.11 169 | 71.05 128 | 46.55 210 | 51.17 140 | 41.83 192 | 48.20 151 | 61.81 179 | 84.00 114 | 81.14 163 | 91.28 167 | 89.42 149 |
|
v13 | | | 69.55 174 | 67.33 180 | 72.14 159 | 75.83 180 | 79.04 203 | 78.22 145 | 64.85 180 | 56.16 173 | 50.60 154 | 43.43 182 | 44.56 173 | 70.05 152 | 79.01 169 | 80.92 169 | 93.28 100 | 88.22 167 |
|
tfpnnormal | | | 69.29 175 | 65.58 183 | 73.62 137 | 79.87 137 | 84.82 164 | 76.97 160 | 75.12 96 | 45.29 212 | 49.03 163 | 35.57 209 | 37.20 210 | 68.02 155 | 82.70 123 | 81.24 156 | 92.69 135 | 92.20 127 |
|
v1240 | | | 69.28 176 | 67.82 175 | 71.00 173 | 74.09 193 | 85.13 161 | 76.54 165 | 67.28 161 | 53.17 188 | 44.70 191 | 41.55 194 | 39.38 202 | 70.51 146 | 81.29 140 | 81.18 160 | 92.88 131 | 93.02 122 |
|
CVMVSNet | | | 68.95 177 | 70.79 145 | 66.79 187 | 79.69 139 | 83.75 172 | 72.05 185 | 70.90 129 | 56.20 172 | 36.30 208 | 54.94 124 | 59.22 116 | 54.03 197 | 78.33 174 | 78.65 180 | 87.77 203 | 84.44 183 |
|
MIMVSNet | | | 68.66 178 | 69.43 159 | 67.76 182 | 64.92 216 | 84.68 166 | 74.16 177 | 54.10 214 | 60.85 144 | 51.27 138 | 39.47 199 | 49.48 146 | 67.48 158 | 84.86 108 | 85.57 87 | 94.63 44 | 81.10 200 |
|
TDRefinement | | | 67.82 179 | 64.91 189 | 71.22 172 | 82.08 124 | 81.45 183 | 77.42 156 | 73.79 105 | 59.62 152 | 48.35 169 | 42.35 189 | 42.40 191 | 60.87 181 | 74.69 193 | 74.64 202 | 84.83 213 | 79.20 203 |
|
anonymousdsp | | | 67.61 180 | 68.94 164 | 66.04 191 | 71.44 205 | 83.97 169 | 66.45 197 | 63.53 185 | 50.54 197 | 42.42 198 | 49.39 142 | 45.63 164 | 62.84 176 | 77.99 177 | 81.34 155 | 89.59 191 | 93.75 117 |
|
TinyColmap | | | 67.16 181 | 63.51 199 | 71.42 169 | 77.94 147 | 79.54 197 | 72.80 182 | 69.78 138 | 56.58 169 | 45.52 184 | 44.53 161 | 33.53 219 | 74.45 108 | 76.91 186 | 77.06 191 | 88.03 202 | 76.41 206 |
|
FC-MVSNet-test | | | 67.04 182 | 72.47 136 | 60.70 208 | 76.92 152 | 81.41 184 | 61.52 205 | 69.45 140 | 65.58 127 | 26.74 226 | 61.79 94 | 60.40 112 | 41.17 217 | 77.60 181 | 77.78 186 | 88.41 198 | 82.70 196 |
|
TransMVSNet (Re) | | | 66.87 183 | 64.30 194 | 69.88 177 | 78.32 143 | 81.35 186 | 73.88 178 | 74.34 104 | 43.19 216 | 45.20 188 | 40.12 196 | 42.37 192 | 55.97 192 | 80.85 143 | 79.15 176 | 91.56 161 | 83.06 193 |
|
CMPMVS | | 50.59 17 | 66.74 184 | 62.72 203 | 71.42 169 | 85.40 112 | 89.72 112 | 72.69 183 | 70.72 131 | 51.24 193 | 51.75 135 | 38.91 202 | 44.40 174 | 63.74 172 | 70.84 207 | 71.52 207 | 84.19 214 | 72.45 216 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
v7n | | | 66.43 185 | 65.51 184 | 67.51 183 | 71.63 204 | 83.10 173 | 70.89 189 | 65.02 177 | 50.13 200 | 44.68 192 | 39.59 198 | 38.77 204 | 62.57 177 | 77.59 182 | 78.91 177 | 90.29 184 | 90.44 140 |
|
EG-PatchMatch MVS | | | 66.23 186 | 65.20 186 | 67.43 184 | 77.74 148 | 86.20 146 | 72.51 184 | 63.68 184 | 43.95 214 | 43.44 195 | 36.22 208 | 45.43 165 | 54.04 196 | 81.00 141 | 80.95 168 | 93.15 115 | 82.67 197 |
|
v52 | | | 65.34 187 | 64.59 191 | 66.21 189 | 69.63 209 | 82.41 179 | 69.22 190 | 62.80 187 | 49.63 201 | 45.15 189 | 39.31 201 | 41.85 193 | 60.68 183 | 72.61 197 | 77.02 193 | 89.75 190 | 89.33 152 |
|
V4 | | | 65.34 187 | 64.59 191 | 66.21 189 | 69.64 208 | 82.42 178 | 69.22 190 | 62.80 187 | 49.60 202 | 45.21 187 | 39.33 200 | 41.82 194 | 60.66 184 | 72.61 197 | 77.03 192 | 89.76 189 | 89.32 154 |
|
v748 | | | 65.00 189 | 63.86 198 | 66.33 188 | 71.85 202 | 82.15 181 | 66.80 195 | 65.64 172 | 48.50 206 | 47.98 171 | 39.62 197 | 39.20 203 | 56.44 191 | 71.25 204 | 77.53 188 | 89.29 192 | 88.74 164 |
|
WR-MVS | | | 64.98 190 | 66.59 182 | 63.09 200 | 74.34 191 | 82.68 176 | 64.98 203 | 69.17 143 | 54.42 183 | 36.18 209 | 44.32 166 | 44.35 175 | 44.65 207 | 73.60 194 | 77.83 185 | 89.21 194 | 88.96 160 |
|
gm-plane-assit | | | 64.86 191 | 68.15 172 | 61.02 207 | 76.44 164 | 68.29 217 | 41.60 226 | 53.37 215 | 34.68 224 | 26.19 228 | 33.22 212 | 57.09 125 | 71.97 120 | 95.12 4 | 93.97 6 | 96.54 13 | 94.66 89 |
|
CP-MVSNet | | | 64.84 192 | 64.97 187 | 64.69 195 | 72.09 199 | 81.04 188 | 66.66 196 | 67.53 158 | 52.45 190 | 37.40 204 | 44.00 173 | 38.37 206 | 53.54 199 | 72.26 201 | 76.93 194 | 90.94 175 | 89.75 145 |
|
MDTV_nov1_ep13_2view | | | 64.72 193 | 64.94 188 | 64.46 196 | 71.14 206 | 81.94 182 | 67.53 193 | 54.54 211 | 55.92 175 | 43.29 196 | 44.02 172 | 43.27 184 | 59.87 185 | 71.85 203 | 74.77 201 | 90.36 182 | 82.82 195 |
|
MVS-HIRNet | | | 64.63 194 | 64.03 197 | 65.33 193 | 75.01 187 | 82.84 174 | 58.54 213 | 52.10 217 | 55.42 178 | 49.29 161 | 29.83 218 | 43.48 181 | 66.97 162 | 78.28 175 | 78.81 178 | 90.07 187 | 79.52 202 |
|
LTVRE_ROB | | 63.07 16 | 64.49 195 | 63.16 202 | 66.04 191 | 77.47 150 | 82.64 177 | 70.98 188 | 65.02 177 | 34.01 225 | 29.61 218 | 49.12 143 | 35.58 215 | 70.57 144 | 75.10 191 | 78.45 182 | 82.60 217 | 87.24 173 |
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016 |
PEN-MVS | | | 64.35 196 | 64.29 195 | 64.42 197 | 72.67 195 | 79.83 193 | 66.97 194 | 68.24 149 | 51.21 194 | 35.29 211 | 44.09 170 | 38.51 205 | 52.36 202 | 71.06 205 | 77.65 187 | 90.99 171 | 87.68 171 |
|
pmmvs6 | | | 64.24 197 | 61.77 207 | 67.12 185 | 72.39 198 | 81.39 185 | 71.33 187 | 65.95 169 | 36.05 221 | 48.48 168 | 30.55 214 | 43.45 182 | 58.75 187 | 77.88 180 | 76.36 197 | 85.83 210 | 86.70 177 |
|
pmmvs-eth3d | | | 64.24 197 | 61.96 205 | 66.90 186 | 66.35 213 | 76.04 209 | 66.09 199 | 66.31 165 | 52.59 189 | 50.94 146 | 37.61 204 | 32.79 221 | 62.43 178 | 75.78 189 | 75.48 199 | 89.27 193 | 83.39 191 |
|
PS-CasMVS | | | 64.22 199 | 64.19 196 | 64.25 198 | 71.86 201 | 80.67 189 | 66.42 198 | 67.43 160 | 50.64 196 | 36.48 206 | 42.60 186 | 37.46 209 | 52.56 201 | 71.98 202 | 76.69 196 | 90.76 176 | 89.29 155 |
|
WR-MVS_H | | | 64.14 200 | 65.36 185 | 62.71 202 | 72.47 197 | 82.33 180 | 65.13 200 | 66.99 162 | 51.81 192 | 36.47 207 | 43.33 183 | 42.77 188 | 43.99 209 | 72.41 200 | 75.99 198 | 91.20 169 | 88.86 163 |
|
SixPastTwentyTwo | | | 63.75 201 | 63.42 200 | 64.13 199 | 72.91 194 | 80.34 191 | 61.29 206 | 63.90 182 | 49.58 203 | 40.42 201 | 54.99 123 | 37.13 211 | 60.90 180 | 68.46 211 | 70.80 210 | 85.37 212 | 82.65 198 |
|
PM-MVS | | | 63.52 202 | 62.51 204 | 64.70 194 | 64.79 218 | 76.08 208 | 65.07 201 | 62.08 189 | 58.13 159 | 46.56 179 | 44.98 155 | 31.31 222 | 62.89 175 | 72.58 199 | 69.93 214 | 86.81 207 | 84.55 182 |
|
DTE-MVSNet | | | 63.26 203 | 63.41 201 | 63.08 201 | 72.59 196 | 78.56 204 | 65.03 202 | 68.28 148 | 50.53 198 | 32.38 215 | 44.03 171 | 37.79 208 | 49.48 205 | 70.83 208 | 76.73 195 | 90.73 177 | 85.42 180 |
|
testgi | | | 63.11 204 | 64.88 190 | 61.05 206 | 75.83 180 | 78.51 205 | 60.42 208 | 66.20 166 | 48.77 205 | 34.56 213 | 56.96 107 | 40.35 198 | 40.95 218 | 77.46 183 | 77.22 190 | 88.37 200 | 74.86 213 |
|
GG-mvs-BLEND | | | 62.08 205 | 88.31 37 | 31.46 230 | 0.16 238 | 98.10 6 | 91.57 36 | 0.09 236 | 85.07 58 | 0.21 241 | 73.90 49 | 83.74 33 | 0.19 238 | 88.98 58 | 89.39 51 | 96.58 12 | 99.02 11 |
|
Anonymous20231206 | | | 62.05 206 | 61.83 206 | 62.30 204 | 72.09 199 | 77.84 206 | 63.10 204 | 67.62 157 | 50.20 199 | 36.68 205 | 29.59 219 | 37.05 212 | 43.90 210 | 77.33 184 | 77.31 189 | 90.41 181 | 83.49 190 |
|
N_pmnet | | | 60.52 207 | 58.83 211 | 62.50 203 | 68.97 210 | 75.61 210 | 59.72 211 | 66.47 163 | 51.90 191 | 41.26 199 | 35.42 210 | 35.63 214 | 52.25 203 | 67.07 215 | 70.08 213 | 86.35 208 | 76.10 207 |
|
LP | | | 59.72 208 | 58.23 212 | 61.44 205 | 75.67 184 | 74.97 211 | 61.05 207 | 48.34 223 | 54.02 184 | 40.82 200 | 31.61 213 | 36.92 213 | 54.69 193 | 67.52 213 | 71.18 209 | 88.08 201 | 71.42 219 |
|
testpf | | | 59.38 209 | 64.51 193 | 53.40 216 | 76.71 155 | 66.40 219 | 50.18 220 | 38.98 233 | 64.13 135 | 35.10 212 | 47.91 149 | 51.41 135 | 43.16 211 | 66.37 216 | 71.23 208 | 76.25 225 | 84.14 188 |
|
EU-MVSNet | | | 58.73 210 | 60.92 208 | 56.17 212 | 66.17 214 | 72.39 214 | 58.85 212 | 61.24 192 | 48.47 207 | 27.91 223 | 46.70 152 | 40.06 199 | 39.07 219 | 68.27 212 | 70.34 212 | 83.77 215 | 80.23 201 |
|
test2356 | | | 58.43 211 | 59.52 209 | 57.16 210 | 66.71 212 | 68.00 218 | 54.69 215 | 60.91 195 | 49.22 204 | 28.63 221 | 41.86 191 | 33.68 218 | 44.36 208 | 72.98 195 | 75.47 200 | 87.69 204 | 75.40 210 |
|
test20.03 | | | 57.93 212 | 59.22 210 | 56.44 211 | 71.84 203 | 73.78 213 | 53.55 217 | 65.96 168 | 43.02 217 | 28.46 222 | 37.50 205 | 38.17 207 | 30.41 226 | 75.25 190 | 74.42 204 | 88.41 198 | 72.37 217 |
|
testus | | | 55.91 213 | 56.38 213 | 55.37 214 | 65.15 215 | 65.88 221 | 50.07 221 | 60.92 194 | 45.62 211 | 26.99 225 | 41.74 193 | 24.43 228 | 42.08 214 | 69.50 210 | 73.60 205 | 86.97 206 | 73.91 214 |
|
MDA-MVSNet-bldmvs | | | 54.99 214 | 52.66 216 | 57.71 209 | 52.74 229 | 74.87 212 | 55.61 214 | 68.41 147 | 43.65 215 | 32.54 214 | 37.93 203 | 22.11 230 | 54.11 195 | 48.85 228 | 67.34 217 | 82.85 216 | 73.88 215 |
|
new-patchmatchnet | | | 53.91 215 | 52.69 215 | 55.33 215 | 64.83 217 | 70.90 215 | 52.24 219 | 61.75 190 | 41.09 218 | 30.82 216 | 29.90 217 | 28.22 224 | 36.69 221 | 61.52 221 | 65.08 220 | 85.64 211 | 72.14 218 |
|
MIMVSNet1 | | | 52.76 216 | 53.95 214 | 51.38 219 | 41.96 233 | 70.79 216 | 53.56 216 | 63.03 186 | 39.36 219 | 27.83 224 | 22.73 228 | 33.07 220 | 34.47 223 | 70.49 209 | 72.69 206 | 87.41 205 | 68.51 220 |
|
pmmvs3 | | | 52.59 217 | 52.43 217 | 52.78 217 | 54.53 227 | 64.49 223 | 50.07 221 | 46.89 226 | 35.31 223 | 30.19 217 | 27.27 221 | 26.96 226 | 53.02 200 | 67.28 214 | 70.54 211 | 81.96 218 | 75.20 211 |
|
new_pmnet | | | 50.32 218 | 51.36 218 | 49.11 220 | 49.19 230 | 64.89 222 | 48.66 224 | 47.99 225 | 47.55 208 | 26.27 227 | 29.51 220 | 28.66 223 | 44.89 206 | 61.12 222 | 62.74 224 | 77.66 224 | 65.03 223 |
|
FPMVS | | | 50.25 219 | 45.67 224 | 55.58 213 | 70.48 207 | 60.12 224 | 59.78 210 | 59.33 200 | 46.66 209 | 37.94 202 | 30.22 216 | 27.51 225 | 35.94 222 | 50.98 227 | 47.90 227 | 70.02 228 | 56.31 225 |
|
Anonymous20231211 | | | 49.72 220 | 47.45 221 | 52.38 218 | 60.54 221 | 66.16 220 | 52.47 218 | 60.87 196 | 25.32 231 | 25.16 229 | 15.98 230 | 23.66 229 | 37.00 220 | 61.01 223 | 64.41 222 | 78.25 223 | 75.60 208 |
|
1111 | | | 48.34 221 | 47.93 220 | 48.83 221 | 58.14 223 | 59.33 226 | 37.54 227 | 43.85 227 | 31.76 226 | 29.36 219 | 23.26 225 | 34.58 216 | 42.20 212 | 65.15 217 | 68.72 216 | 81.86 219 | 52.66 228 |
|
testmv | | | 46.89 222 | 46.37 222 | 47.48 222 | 60.96 219 | 58.36 228 | 36.71 229 | 56.94 204 | 27.16 229 | 17.93 233 | 23.94 223 | 18.84 232 | 31.06 224 | 61.55 219 | 66.72 218 | 81.28 220 | 68.05 221 |
|
test1235678 | | | 46.88 223 | 46.36 223 | 47.48 222 | 60.96 219 | 58.35 229 | 36.71 229 | 56.94 204 | 27.15 230 | 17.93 233 | 23.93 224 | 18.82 233 | 31.06 224 | 61.55 219 | 66.71 219 | 81.27 221 | 68.04 222 |
|
test12356 | | | 41.15 224 | 41.46 225 | 40.78 225 | 53.10 228 | 49.87 230 | 33.37 232 | 52.25 216 | 25.12 232 | 15.64 235 | 22.76 227 | 15.01 234 | 15.81 231 | 52.97 225 | 64.54 221 | 74.50 227 | 59.96 224 |
|
PMVS | | 36.83 18 | 40.62 225 | 36.39 226 | 45.56 224 | 58.40 222 | 33.20 234 | 32.62 233 | 56.02 206 | 28.25 228 | 37.92 203 | 22.29 229 | 26.15 227 | 25.29 228 | 48.49 229 | 43.82 230 | 63.13 231 | 52.53 229 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
Gipuma | | | 35.20 226 | 33.96 227 | 36.65 227 | 43.30 232 | 32.51 235 | 26.96 235 | 48.31 224 | 38.87 220 | 20.08 232 | 8.08 233 | 7.41 238 | 26.44 227 | 53.60 224 | 58.43 225 | 54.81 233 | 38.79 232 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
.test1245 | | | 33.05 227 | 31.21 229 | 35.20 228 | 58.14 223 | 59.33 226 | 37.54 227 | 43.85 227 | 31.76 226 | 29.36 219 | 23.26 225 | 34.58 216 | 42.20 212 | 65.15 217 | 0.77 234 | 0.11 238 | 3.62 236 |
|
PMMVS2 | | | 32.52 228 | 33.92 228 | 30.88 231 | 34.15 236 | 44.70 233 | 27.79 234 | 39.69 232 | 22.21 233 | 4.31 240 | 15.73 231 | 14.13 235 | 12.45 235 | 40.11 230 | 47.00 228 | 66.88 229 | 53.54 226 |
|
no-one | | | 32.08 229 | 31.09 230 | 33.23 229 | 46.10 231 | 46.90 232 | 20.80 236 | 49.13 221 | 16.27 234 | 7.85 237 | 10.62 232 | 10.68 236 | 13.65 234 | 31.50 232 | 51.31 226 | 61.83 232 | 50.38 230 |
|
E-PMN | | | 21.42 230 | 17.56 232 | 25.94 232 | 36.25 235 | 19.02 238 | 11.56 237 | 43.72 230 | 15.25 236 | 6.99 238 | 8.04 234 | 4.53 240 | 21.77 230 | 16.13 234 | 26.16 232 | 35.34 235 | 33.77 233 |
|
MVE | | 25.07 19 | 21.25 231 | 23.51 231 | 18.62 234 | 15.07 237 | 29.77 237 | 10.67 239 | 34.60 234 | 12.51 237 | 9.46 236 | 7.84 235 | 3.82 241 | 14.38 233 | 27.45 233 | 42.42 231 | 27.56 237 | 40.74 231 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
EMVS | | | 20.61 232 | 16.32 233 | 25.62 233 | 36.41 234 | 18.93 239 | 11.51 238 | 43.75 229 | 15.65 235 | 6.53 239 | 7.56 236 | 4.68 239 | 22.03 229 | 14.56 235 | 23.10 233 | 33.51 236 | 29.77 234 |
|
testmvs | | | 0.76 233 | 1.23 234 | 0.21 235 | 0.05 239 | 0.21 240 | 0.38 241 | 0.09 236 | 0.94 238 | 0.05 242 | 2.13 238 | 0.08 242 | 0.60 237 | 0.82 236 | 0.77 234 | 0.11 238 | 3.62 236 |
|
test123 | | | 0.67 234 | 1.11 235 | 0.16 236 | 0.01 240 | 0.14 241 | 0.20 242 | 0.04 238 | 0.77 239 | 0.02 243 | 2.15 237 | 0.02 243 | 0.61 236 | 0.23 237 | 0.72 236 | 0.07 240 | 3.76 235 |
|
sosnet-low-res | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 244 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
sosnet | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 244 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
ambc | | | | 50.35 219 | | 55.61 226 | 59.93 225 | 48.73 223 | | 44.08 213 | 35.81 210 | 24.01 222 | 10.64 237 | 41.57 216 | 72.83 196 | 63.35 223 | 74.99 226 | 77.61 204 |
|
MTAPA | | | | | | | | | | | 91.14 2 | | 85.84 20 | | | | | |
|
MTMP | | | | | | | | | | | 90.95 3 | | 84.13 29 | | | | | |
|
Patchmatch-RL test | | | | | | | | 8.17 240 | | | | | | | | | | |
|
tmp_tt | | | | | 39.78 226 | 56.31 225 | 31.71 236 | 35.84 231 | 15.08 235 | 82.57 65 | 50.83 149 | 63.07 87 | 47.51 153 | 15.28 232 | 52.23 226 | 44.24 229 | 65.35 230 | |
|
XVS | | | | | | 89.65 64 | 95.93 42 | 85.97 71 | | | 76.32 47 | | 82.05 39 | | | | 93.51 83 | |
|
X-MVStestdata | | | | | | 89.65 64 | 95.93 42 | 85.97 71 | | | 76.32 47 | | 82.05 39 | | | | 93.51 83 | |
|
abl_6 | | | | | 89.54 27 | 95.55 33 | 97.59 15 | 89.01 50 | 85.00 34 | 94.67 11 | 83.04 28 | 84.70 31 | 91.47 3 | 89.46 20 | | | 95.20 35 | 98.63 17 |
|
mPP-MVS | | | | | | 95.90 28 | | | | | | | 80.22 47 | | | | | |
|
NP-MVS | | | | | | | | | | 89.55 41 | | | | | | | | |
|
Patchmtry | | | | | | | 87.41 124 | 78.32 141 | 54.14 212 | | 51.09 141 | | | | | | | |
|
DeepMVS_CX | | | | | | | 48.96 231 | 43.77 225 | 40.58 231 | 50.93 195 | 24.67 230 | 36.95 207 | 20.18 231 | 41.60 215 | 38.92 231 | | 52.37 234 | 53.31 227 |
|